Evolutionary many-objective optimization by MO-NSGA-II with enhanced mating selection

Author

Shao-Wen Chen ; Tsung-Che Chiang

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Normal Univ., Taipei, Taiwan

fYear

2014

fDate

6-11 July 2014

Firstpage

1397

Lastpage

1404

Abstract

Many-objective optimization deals with problems with more than three objectives. The rapid growth of non-dominated solutions with the increase of the number of objectives weakens the search ability of Pareto-dominance-based multiobjective evolutionary algorithms. MO-NSGA-II strengthens its dominance-based predecessor, NSGA-II, by guiding the search process with reference points. In this paper, we further improve MO-NSGA-II by enhancing its mating selection mechanism with a hierarchical selection and a neighborhood concept based on the reference points. Experimental results confirm that the proposed ideas lead to better solution quality.

Keywords

Pareto optimisation; genetic algorithms; search problems; MO-NSGA-II; Pareto-dominance-based multiobjective evolutionary algorithms; evolutionary many-objective optimization; hierarchical selection; mating selection mechanism enhancement; neighborhood concept; nondominated solutions; reference points; search ability; search process; Convergence; Evolutionary computation; Pareto optimization; Sociology; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation (CEC), 2014 IEEE Congress on

Conference_Location

Beijing

Print_ISBN

978-1-4799-6626-4

Type

conf

DOI

10.1109/CEC.2014.6900400

Filename

6900400